Three-dimensional (3-D) radiographic volume imaging is a diagnostic tool that offers advantages over earlier two-dimensional (2-D) radiographic imaging techniques for evaluating the condition of internal structures and organs. 3-D imaging of a patient or other subject has been made possible by a number of advancements, including the development of high-speed imaging detectors, such as digital radiography (DR) detectors that enable multiple images to be taken in rapid succession. To meet various requirements for imaging of different portions of the human anatomy under different conditions, a number of types of radiographic volume imaging apparatus have been developed. These include computed tomography (CT) apparatus such as cone-beam computed tomography (CBCT) systems, as well as others.
Among the challenges addressed by these developing 3-D imaging technologies is the need for improved image reconstruction techniques. In radiographic 3-D imaging systems, a series of 2-D images, taken at different angles with relation to the subject, is processed in order to generate or reconstruct image content in 3-D image space. Conventional reconstruction algorithms used for CBCT and other 3-D imaging systems include analytical techniques such as FDK reconstruction, filtered back projection (FBP), and others.
One type of reconstruction is model based iterative reconstruction (MBIR) technique. MBIR allows the reconstruction logic to take advantage of prior knowledge of the imaged subject and accurate statistical, geometrical, and physical system modeling of the capture process. Iterative reconstruction techniques, based on progressive estimation methods used to address large-scale problems in numerical linear algebra, are computationally intensive. In addition, MBIR techniques allow reconstructions using fewer 2-D projection images and/or reduce the dose per projection, thereby reducing the radiation dose levels for the patient.
MBIR reconstruction employs an objective function with a statistically weighted data fidelity term and an often highly nonlinear regularization/prior knowledge term. In processing, the image is reconstructed by computing an estimate that minimizes the objective function.
Applicants have recognized that MBIR techniques are hampered by their time-consuming and computationally intensive characteristics. For example, convergence speed can be slow when compared against competing analytical reconstruction techniques.
Reference is made to patent literature: U.S. 2014/0369580 (Yu); U.S. 2015/0086097 (Chen); U.S. 2011/0164031 (Shi); and U.S. Pat. No. 8,189,735 (Khare).
Reference is also made to non-patent literature:    (a) Abolfazi Mehranian, Mohammad Reza Ay, Fotis Kotasidis, Habib Zaidi, “An ordered-subsets proximal preconditioned gradient algorithm for edge-preserving PET image reconstruction”, Med. Phys. 40(5), May 2013 pp. 052503-1 to -14.    (b) Emil Y. Sidky and Xiaochuan Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization” Physics in Medicine and Biology 53 (2008) pp. 4777-4807.    (c) Yongsheng Pan, Ross Whitaker, Arvi Cheryauka, Dave Ferguson, “TV-regularized Iterative Image Reconstruction on a Mobile C-ARM CT” SPIE Proceedings, Medical Imaging March 2010 pp 1-12.    (d) Yongsheng Pan, Ross Whitaker, Arvi Cheryauka, Dave Ferguson, “Feasibility of GPU-assisted iterative image reconstruction for mobile C-arm CT” SPIE Proceedings, Medical Imaging 2009, Vol 7258, pp. 72585J-1 to -9.
Applicants have recognized that MBIR reconstruction methods that speed convergence of the iterative processing and that provide improved noise uniformity over conventional methods would be useful for time and cost savings as well as for reduced radiation exposure to the patient.